Triple

T3270204
Position Surface form Disambiguated ID Type / Status
Subject Philip William, Prince of Orange E68627 entity
Predicate birthPlace P1 FINISHED
Object Buren E328878 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Buren | Statement: [Philip William, Prince of Orange, birthPlace, Buren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buren
Context triple: [Philip William, Prince of Orange, birthPlace, Buren]
  • A. Buren chosen
    Buren is a historic Dutch town in the province of Gelderland, known for its ties to the Dutch royal family and its well-preserved medieval character.
  • B. Montesson
    Montesson is a suburban commune in the Yvelines department of north-central France, located to the northwest of Paris along the Seine River.
  • C. Boissière
    Boissière is a Paris Métro station on the city’s Right Bank, located in the 16th arrondissement near the Trocadéro area.
  • D. Sauvy
    Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
  • E. Morangis
    Morangis is a commune in the southern suburbs of Paris, located in the Essonne department in the Île-de-France region of northern France.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ad859b54f881909bf530d549caf2fd completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adaff349148190beae8c0994b7ad83 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b28efded588190bd6c361e5298b496 completed March 12, 2026, 10:01 a.m.
Created at: March 8, 2026, 3:09 p.m.